To demonstrate this, let us consider the MobileNetV3 (1.2\(\times \) ) search space.
First, we build and train a DONNA accuracy predictor for full-precision (FP) networks and then test this predictor for networks with weights and activations quantized to 8 bits (INT8).
The search space includes k \(\in \lbrace 3,5,7\rbrace \) ; expand \(\in \lbrace 3,4,6\rbrace \) ; depth \(\in \lbrace 2,3,4\rbrace \) ; activation \(\in \lbrace ReLU/Swish\rbrace \) ; attention \(\in \lbrace None/SE\rbrace \) ; and channel-scaling \(\in \lbrace 0.5\times , 1.0\times \rbrace \) . We build a complete Block Library in FP; sampling 43 FP networks as an Architecture Library and finetuning them to collect the training data for the FP accuracy predictor model.
Second, we quantize the Block Library using the Data-Free-Quantization (DFQ) [1]} post training quantization method using 8 bits weights and activations (INT8). The quantized Block Library now provides the quality metrics for quantized blocks, which can be used as inputs to the FP accuracy predictor to predict INT8 accuracy.
Finally, we test the FP accuracy predictor model on a test set of INT8 networks. For this, we sample 20 networks whose INT8-block quality is within the range of the train set of the accuracy predictor.
These networks are first finetuned in FP using the procedure outlined in section  and then quantized to INT8 using DFQ [1]}.
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